Pre-trained Models (PTMs) have facilitated substantial progress in the field
of Speech Emotion Recognition (SER). SER is an area with applications ranging
from HumanComputer Interaction to Healthcare. Recent studies have leveraged
various PTM representations as input features for downstream models for SER.
PTM specifically pre-trained for paralinguistic tasks have obtained
state-of-the-art (SOTA) performance for SER. However, such PTM haven't been
evaluated for SER in multilingual settings and experimented only with English.
So, we fill this gap, by performing a comprehensive comparative study of five
PTMs (TRILLsson, wav2vec2, XLS-R, x-vector, Whisper) for assessing the
effectiveness of paralingual PTM (TRILLsson) for SER across multiple languages.
Representations from TRILLsson achieved the best performance among all the
PTMs. This demonstrates that TRILLsson is able to effectively capture the
various paralinguistic features from speech data for better SER. We also show
that downstream models using TRILLsson representations achieve SOTA performance
in terms of accuracy across various multi-lingual datasets.

我们进行了五个预训练模型的综合比较研究，评估了 TRILLsson 对多种语言的语音情感识别的效果，并表明 TRILLsson 能够有效地捕捉语音数据中的各种语音特征，从而实现更好的语音情感识别性能。